Video Deraining Using the Visual Properties of Rain Streaks

In computer vision applications, the visibility of the video content is crucial to perform analysis for better accuracy. The visibility can be affected by several atmospheric interferences in challenging weather – one such interference is the appearance of rain streaks. Recently, rain streak removal has achieved plenty of interest among researchers, as it has some exciting applications such as autonomous cars, intelligent traffic monitoring systems, multimedia, etc. In this paper, we propose a novel and simple method of rain streak removal by combining three novel extracted visual features focusing on the temporal appearance, wide shape and relative location of the rain streak. We called it the TAWL (Temporal Appearance, Width, and Location) method. The proposed TAWL method adaptively uses features from different resolutions and frame rates. Moreover, it progressively processes features from the upcoming frames so that it can remove rain in real-time. Experiments have been conducted using video sequences with both real rain and synthetic rain to compare the performance of the proposed method against the relevant state-of-the-art methods. The experimental results demonstrate that the proposed method outperforms the state-of-the-art methods by removing more rain streaks while keeping other moving regions.